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Charles Ivan Niswander II
By the end of this project-based course, you will learn the basics of the R programming language. I will start with the basics such as installing new library packages with CRAN, until we get you started with some data manipulations tasks. I'll introduce you to the most commonly used statistical functions of R, and finally we'll code a very simple Neural Network in R which we'll analyze cereal using an open-source dataset from Carnegie Mellon University (CMU). This project will provide valuable experience in your Machine Learning and Artificial Intelligence development journey.
Familiarity with programming fundamentals is heavily...
Read more
By the end of this project-based course, you will learn the basics of the R programming language. I will start with the basics such as installing new library packages with CRAN, until we get you started with some data manipulations tasks. I'll introduce you to the most commonly used statistical functions of R, and finally we'll code a very simple Neural Network in R which we'll analyze cereal using an open-source dataset from Carnegie Mellon University (CMU). This project will provide valuable experience in your Machine Learning and Artificial Intelligence development journey.
Familiarity with programming fundamentals is heavily...
Read more
By the end of this project-based course, you will learn the basics of the R programming language. I will start with the basics such as installing new library packages with CRAN, until we get you started with some data manipulations tasks. I'll introduce you to the most commonly used statistical functions of R, and finally we'll code a very simple Neural Network in R which we'll analyze cereal using an open-source dataset from Carnegie Mellon University (CMU). This project will provide valuable experience in your Machine Learning and Artificial Intelligence development journey.
Familiarity with programming fundamentals is heavily recommended.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/i246ar/getting
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the R programming basics for beginners
Introduces important statistical functions of R
Provides hands-on experience in coding a simple Neural Network in R
Uses an open-source dataset from Carnegie Mellon University (CMU), adding relevance and authenticity
Course is best suited for learners in the North America region
Familiarity with programming fundamentals is highly recommended
Save this course
Save Getting Started with R Programming to your list so you can find it easily later:
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Reviews summary
Getting started with r programming basics
This introductory course to R programming starts with the most basic installation and gets you started with some data manipulation tasks. Students will be introduced to the most commonly used statistical functions of R and will code a simple Neural Network in R which analyzes cereal using an open-source dataset. Familiarity with programming fundamentals is heavily recommended. The course is focused towards learners based out of North America.
Some data manipulation tasks are covered.
Course starts from the very basics.
"I will start with the basics such as installing new library packages with CRAN, until we get you started with some data manipulations tasks."
Introduction to most common statistical functions.
"I'll introduce you to the most commonly used statistical functions of R"
Simple Neural Network coding project.
"we'll code a very simple Neural Network in R which we'll analyze cereal using an open-source dataset from Carnegie Mellon University (CMU)."
Focus towards North American learners.
"Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions."
Gets difficult towards the end.
"But gets impossible to follow in the end."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Getting Started with R Programming with these
activities:
Join a Study Group or Online Forum
Show steps
Connect with fellow learners by joining a study group or engaging in online forums. This will provide opportunities to ask questions, share insights, and learn from others, enhancing your overall understanding of the course material.
Show steps
-
Identify and join a relevant study group or online forum.
-
Participate in discussions and ask questions.
-
Help others by answering their questions.
Review 'The R Book' by Michael J. Crawley
Show steps
Gain a comprehensive understanding of R programming fundamentals by reviewing 'The R Book'. This book covers essential concepts, data manipulation techniques, statistical modeling, and graphics, providing a solid foundation for your learning journey.
View
The R Book
on Amazon
Show steps
-
Read through the book's chapters.
-
Work through the practice exercises.
-
Refer back to the book for reference as needed.
Practice Data Manipulation with dplyr
Show steps
Sharpen your data manipulation skills by practicing with dplyr. This will enhance your ability to clean, transform, and analyze data efficiently, which is a crucial aspect of data analysis in R.
Browse courses on
Data Manipulation
Show steps
-
Install and load the dplyr library.
-
Load a dataset into R.
-
Work through exercises involving filtering, selecting, and transforming data.
-
Review the dplyr documentation for reference.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice R Coding Challenges on LeetCode
Show steps
Enhance your R programming skills by solving coding challenges on LeetCode. This will help you improve your problem-solving abilities, strengthen your understanding of R syntax, and prepare you for real-world coding scenarios.
Browse courses on
R Programming
Show steps
-
Sign up for a LeetCode account.
-
Start solving easy R coding challenges.
-
Gradually move on to more difficult challenges.
-
Review your solutions and learn from your mistakes.
Create a Data Visualization Project
Show steps
Solidify your understanding of data visualization techniques by creating a project using ggplot2. This project will allow you to apply your knowledge, explore data, and communicate insights effectively through visual representations.
Browse courses on
Data Visualization
Show steps
-
Gather a dataset of interest.
-
Explore the data and identify key insights.
-
Design and create visualizations using ggplot2.
-
Write a summary of your findings and insights.
Build a Real-World R Application
Show steps
Challenge yourself by building a real-world R application. This project will provide hands-on experience, encourage creativity, and allow you to apply your skills to solve a practical problem.
Browse courses on
R Programming
Show steps
-
Identify a problem or opportunity.
-
Design and plan your application.
-
Develop the R code for your application.
-
Test and refine your application.
-
Deploy and share your application.
Join a Study Group or Online Forum
Show steps
Connect with fellow learners by joining a study group or engaging in online forums. This will provide opportunities to ask questions, share insights, and learn from others, enhancing your overall understanding of the course material.
Show steps
Show steps
Show steps
- Identify and join a relevant study group or online forum.
- Participate in discussions and ask questions.
- Help others by answering their questions.
Review 'The R Book' by Michael J. Crawley
Show steps
Gain a comprehensive understanding of R programming fundamentals by reviewing 'The R Book'. This book covers essential concepts, data manipulation techniques, statistical modeling, and graphics, providing a solid foundation for your learning journey.
View
The R Book
on Amazon
Show steps
Show steps
Show steps
- Read through the book's chapters.
- Work through the practice exercises.
- Refer back to the book for reference as needed.
Practice Data Manipulation with dplyr
Show steps
Sharpen your data manipulation skills by practicing with dplyr. This will enhance your ability to clean, transform, and analyze data efficiently, which is a crucial aspect of data analysis in R.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Install and load the dplyr library.
- Load a dataset into R.
- Work through exercises involving filtering, selecting, and transforming data.
- Review the dplyr documentation for reference.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice R Coding Challenges on LeetCode
Show steps
Enhance your R programming skills by solving coding challenges on LeetCode. This will help you improve your problem-solving abilities, strengthen your understanding of R syntax, and prepare you for real-world coding scenarios.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Sign up for a LeetCode account.
- Start solving easy R coding challenges.
- Gradually move on to more difficult challenges.
- Review your solutions and learn from your mistakes.
Create a Data Visualization Project
Show steps
Solidify your understanding of data visualization techniques by creating a project using ggplot2. This project will allow you to apply your knowledge, explore data, and communicate insights effectively through visual representations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather a dataset of interest.
- Explore the data and identify key insights.
- Design and create visualizations using ggplot2.
- Write a summary of your findings and insights.
Build a Real-World R Application
Show steps
Challenge yourself by building a real-world R application. This project will provide hands-on experience, encourage creativity, and allow you to apply your skills to solve a practical problem.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Identify a problem or opportunity.
- Design and plan your application.
- Develop the R code for your application.
- Test and refine your application.
- Deploy and share your application.
Career center
Learners who complete Getting Started with R Programming will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses programming, statistics, and AI to solve complex business problems. Familiarity with R programming is becoming a requirement for Data Scientists. This course helps you to build a strong foundation in R, giving you an advantage as you begin your journey as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models. R is popular for its machine learning capabilities. This course will provide you with the foundation in R that you need to begin your career as a Machine Learning Engineer.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use their knowledge of data visualization and programming to create visual representations of data. R is one of the most popular programming languages for data visualization. This course will provide you with a solid foundation in R programming, giving you a competitive edge as you enter the field of data visualization.
Data Analyst
Data Analyst
A Data Analyst uses their knowledge of programming, statistics, and data analysis to gather and interpret data to provide actionable insights to businesses. In recent years, having a grasp of R programming has become a highly valued skill for a Data Analyst. This course provides a solid foundation in R programming, which is one of the most popular programming languages for data analysis, making it a perfect course for those who wish to become a Data Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and programming to solve complex business problems. R programming is a valuable tool for building optimization models. This course will introduce you to the basics of R programming, which can help you Excel as an Operations Research Analyst.
Data Engineer
Data Engineer
Data Engineers design and build systems to manage and process data. R programming is a valuable tool for building data pipelines and data warehouses. This course will introduce you to the basics of R programming, which can accelerate your career as a Data Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use programming to build and analyze quantitative models. R is commonly used for quantitative analysis. This course will provide you with a solid foundation in R programming, which can help you Excel as a Quantitative Analyst.
Actuary
Actuary
Actuaries use programming to develop and analyze risk models. R programming is a valuable tool for building actuarial models. This course will provide you with the foundation in R that you need to begin your career as an Actuary.
Statistician
Statistician
Statisticians use their knowledge of statistics and programming to analyze data and draw conclusions. R is a popular programming language for statistical analysis. This course will provide you with a solid foundation in R programming, helping you build a strong foundation as a Statistician.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use their knowledge of healthcare and data analysis to improve the quality and efficiency of healthcare services. R programming is becoming a popular tool for healthcare analysis. This course will provide you with the foundation in R that you need to begin your career as a Healthcare Analyst.
Software Engineer
Software Engineer
Software Engineers use programming to build software applications. R programming is becoming a popular choice for building data-driven applications. This course will introduce you to the basics of R programming, providing you with an advantage as you enter the field.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to help organizations improve their performance. R is a popular tool for building data-driven solutions for businesses. This course will provide you with the foundation in R programming that you need to begin your career as a Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use programming to build and analyze financial models. R has become a popular choice for building financial models. This course will introduce you to the basics of R programming, providing you with an advantage as you enter the field of Financial Analysis.
User Experience Researcher
User Experience Researcher
User Experience Researchers conduct research to improve the user experience of products and services. R programming can be used to analyze user data and conduct user testing. This course will introduce you to the basics of R programming, providing you with an advantage as you enter the field of User Experience Research.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. R programming can be used to analyze product data and conduct market research. This course will introduce you to the basics of R programming, providing you with an advantage as you enter the field of Product Management.
For more career information including salaries, visit:
OpenCourser.com/course/i246ar/getting
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Getting Started with R Programming.
Save
Comprehensive guide to the R programming language, covering everything from the basics to advanced topics such as data manipulation, statistical modeling, and machine learning. It valuable resource for both beginners and experienced R users.
Save
Practical guide to using R for data science. It covers topics such as data cleaning, data exploration, and statistical modeling. It valuable resource for anyone who wants to use R for data analysis.
Comprehensive guide to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to use R for machine learning.
Comprehensive guide to statistical learning. It covers topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone who wants to learn about statistical learning.
Classic textbook on statistical learning. It covers topics such as linear regression, logistic regression, and support vector machines. It valuable resource for anyone who wants to learn about statistical learning.
Save
Practical guide to predictive modeling. It covers topics such as data preprocessing, model selection, and model evaluation. It valuable resource for anyone who wants to use machine learning for predictive analytics.
Comprehensive guide to R programming for data science. It covers topics such as data manipulation, data visualization, and statistical modeling. It valuable resource for anyone who wants to use R for data science.
Practical guide to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to use R for machine learning.
Is an introduction to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn about machine learning with R.
Comprehensive guide to pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn about pattern recognition and machine learning.
Comprehensive guide to machine learning from a probabilistic perspective. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn about machine learning from a probabilistic perspective.
Comprehensive guide to Bayesian data analysis. It covers topics such as Bayesian inference, Bayesian modeling, and Bayesian computation. It valuable resource for anyone who wants to learn about Bayesian data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i246ar/getting
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Teaches the R programming basics for beginners
Introduces important statistical functions of R
Provides hands-on experience in coding a simple Neural Network in R
Uses an open-source dataset from Carnegie Mellon University (CMU), adding relevance and authenticity
Course is best suited for learners in the North America region
Familiarity with programming fundamentals is highly recommended
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